Hitoshi Iida

1.1k citations
56 papers · 396 · h-index 10

Impact in

    • Natural Language Processing Techniques
    • Topic Modeling
    • Speech and dialogue systems
    • Speech Recognition and Synthesis
    • Semantic Web and Ontologies
    • Speech and Audio Processing
    • Music and Audio Processing

Papers in

Hitoshi Iida

46 papers receiving 295 citations

Peers

Hitoshi Iida
Comparison fields: 5 of 41
  • Artificial Intelligence 314
  • Signal Processing 39
  • Experimental and Cognitive Psychology 44
  • Hardware and Architecture 15
  • Software 8
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Jonathan Allen United States
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Citations per year

Countries citing papers authored by Hitoshi Iida

Since Specialization
Citations

This map shows the geographic impact of Hitoshi Iida's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hitoshi Iida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hitoshi Iida more than expected).

Fields of papers citing papers by Hitoshi Iida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hitoshi Iida. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hitoshi Iida. The network helps show where Hitoshi Iida may publish in the future.

Co-authors

The 25 scholars most cited alongside Hitoshi Iida, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hitoshi Iida Line = papers co-authored together Hitoshi Iida links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 199441
2 199839
3 199236
4 199633
5 199432
6 201227
7
Example-Based Machine Translation on Massively Parallel Processors.
199316
8 199815
9 199814
10 199413
11
Heterogeneous computing for example-based translation of spoken language
19959
12 19949
13
Improving performance of transfer-driven machine translation with extra-linguistic information from context, situation and environment
19979
14 20206
15 19826
16 19986
17 19916
18
A method to predict the next utterance using a four-layered plan recognition model
19905
19 20085
20 19975

About Hitoshi Iida

Hitoshi Iida is a scholar working on Artificial Intelligence, Experimental and Cognitive Psychology, Neurology, Psychiatry and Mental health and General Health Professions, having authored 56 papers that have together received 396 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (31 papers), Speech and dialogue systems (24 papers), Topic Modeling (6 papers), Semantic Web and Ontologies (5 papers), Speech Recognition and Synthesis (4 papers), Emotion and Mood Recognition (3 papers), Obsessive-Compulsive Spectrum Disorders (3 papers) and Service-Oriented Architecture and Web Services (3 papers). The work is most often cited by research in Artificial Intelligence (314 citations), Signal Processing (39 citations), Experimental and Cognitive Psychology (44 citations), Hardware and Architecture (15 citations) and Software (8 citations). Hitoshi Iida has collaborated with scholars based in Japan and Germany. Frequent co-authors include Osamu Furuse, Eiichiro Sumita, Sumio Ohno, Tsuyoshi Morimoto, Toshiyuki Takezawa, Yoshinori Sagisaka, Hideki Mima, Fumiaki Sugaya, Nick Campbell and Seiichi Yamamoto. Their work appears in journals such as Speech Communication, Neurologia medico-chirurgica, Journal of Psychiatric Research, Neurosurgery and Language Resources and Evaluation.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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